1. Field
The following description relates generally to the targeting of media content to consumers. More particularly, the embodiments relate to data architecture and processing, and a database solution that will ultimately deliver personalized content, including advertisements, video, audio, audio/visual, text-based content, or any personalized media to a device, where the device can store a delivered identification and is operated by a consumer who is classified in a classification derived from financial information for that consumer.
2. Description of Related Art
Increasing Internet usage where nearly one billion global users access the Internet on at least a monthly basis is reflected by greater corporate spending on Internet advertising and more compelling and effective marketing technologies and practices. For example, Standard and Poor states that keyword search revenues contributed notably to these gains, with 182% growth in 2003, 51% growth in 2004, and 34% growth in 2005, largely reflecting the successes of Google and Yahoo. These companies garner revenues from specific user on-line queries that generate sponsored Internet links and associated click-throughs. This pattern of growth in Internet advertising is anticipated to continue well into the future.
To make their advertising dollars more effective, advertisers attempt to target their advertising to individuals who are more likely to have an interest in the advertised product, thereby producing a higher click-through rate and increased revenues. Of course, in order to target individuals with any degree of accuracy, something must be known about the individual. For this reason, technologies have been developed for what is known in the art as behavioral targeting based on tracking a user's habits through monitoring of the websites that the user visits, and offering targeted advertising based on the content of the visited websites. It is assumed, for example, that if a user is visiting automobile oriented websites, then an automobile oriented advertisement is more likely to generate a user response than one for breakfast cereal. A problem with this type of website tracking is that if an automobile advertisement for a very expensive car is delivered to a user and he cannot afford to purchase the automobile, then this advertisement is not very effective.
While the above-described behavioral profiling has been somewhat effective in improving the effectiveness of Internet advertising, such behavioral profiling is unable to accumulate data related to an individual's particular spending habits. For this reason, some marketers have developed methods to retain customers who have initiated purchases from them by tracking their purchasing habits and trends. However, this tracking of purchases is limited to knowledge of purchases placed on the marketer's own websites. While a marketer such as, e.g., Amazon might track a consumer's shopping habits so that when the consumer logs into an Amazon website at a future time, advertisements can be automatically placed showing suggested items for the consumer to consider based on their previous purchasing habits. Of course, Amazon would have no knowledge of the customer's purchasing habits at retailers not affiliated with Amazon. Therefore, the tracking methodologies used by individual on-line retailers are limited in the benefit that they can provide to the retailer.
Furthermore, when a customer calls in to a business (e.g., a bank or the like) and is placed on hold, advertisements may be presented to the customer during the on-hold period. However, such advertisements are not targeted to the specific customer, and therefore are inefficient.
Accordingly, there is an unmet need for systems and/or methods that facilitate presenting targeted advertisements to a customer over the phone while the customer is on hold, and the like, while overcoming the aforementioned deficiencies.